Dual-stream transformer approach for pain assessment using visual-physiological data modeling.

ABSTRACT: Automatic pain assessment involves accurately recognizing and quantifying pain, dependent on the data modality that may originate from various sources such as video and physiological signals. Traditional pain assessment methods rely on subjective self-reporting, which limits their objectivity, consistency, and overall effectiveness in clinical settings. While machine learning offers a promising alternative, many existing approaches rely on a single data modality, which may not adequately capture the multifaceted nature of pain-related responses. In contrast, multimodal approaches can provide a more comprehensive understanding by integrating diverse sources of information. To address this, we propose a dual-stream framework for classifying physiological and behavioral correlates of pain tha

SUBMITTER: Nguyen MD 

PROVIDER: S-EPMC12453799 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature